From 1a53f502d7fe2b3dfef5d39e511b1834837e2ebb Mon Sep 17 00:00:00 2001
From: SevgiAkten <SevgiAkten@users.noreply.github.com>
Date: Tue, 10 Dec 2024 07:26:13 +0000
Subject: [PATCH] deploy: b764675b314ae2a56fcd34c4e49a4ab670ff93bc

---
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diff --git a/_modules/index.html b/_modules/index.html
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-<li class="toctree-l2"><a class="reference internal" href="../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#usage">Usage</a></li>
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diff --git a/_modules/pycellga/example/example_alpha_cga.html b/_modules/pycellga/example/example_alpha_cga.html
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
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index cc20ca2..e8e0949 100644
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diff --git a/_modules/pycellga/example/example_cga.html b/_modules/pycellga/example/example_cga.html
index e198863..7e21ee7 100644
--- a/_modules/pycellga/example/example_cga.html
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@@ -291,23 +291,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
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diff --git a/_modules/pycellga/example/example_mcccga.html b/_modules/pycellga/example/example_mcccga.html
index 6a30b4e..76c05b7 100644
--- a/_modules/pycellga/example/example_mcccga.html
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@@ -291,23 +291,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/example/example_sync_cga.html b/_modules/pycellga/example/example_sync_cga.html
index 37e9f55..f8c70b4 100644
--- a/_modules/pycellga/example/example_sync_cga.html
+++ b/_modules/pycellga/example/example_sync_cga.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/grid.html b/_modules/pycellga/grid.html
index b83f387..2a751d3 100644
--- a/_modules/pycellga/grid.html
+++ b/_modules/pycellga/grid.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/individual.html b/_modules/pycellga/individual.html
index 5ca3c82..c543b7f 100644
--- a/_modules/pycellga/individual.html
+++ b/_modules/pycellga/individual.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/bit_flip_mutation.html b/_modules/pycellga/mutation/bit_flip_mutation.html
index 6d45c50..f029a01 100644
--- a/_modules/pycellga/mutation/bit_flip_mutation.html
+++ b/_modules/pycellga/mutation/bit_flip_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/byte_mutation.html b/_modules/pycellga/mutation/byte_mutation.html
index 050f936..beffa2f 100644
--- a/_modules/pycellga/mutation/byte_mutation.html
+++ b/_modules/pycellga/mutation/byte_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/byte_mutation_random.html b/_modules/pycellga/mutation/byte_mutation_random.html
index e5d31fa..1b87eea 100644
--- a/_modules/pycellga/mutation/byte_mutation_random.html
+++ b/_modules/pycellga/mutation/byte_mutation_random.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/float_uniform_mutation.html b/_modules/pycellga/mutation/float_uniform_mutation.html
index df36d9c..9a1432a 100644
--- a/_modules/pycellga/mutation/float_uniform_mutation.html
+++ b/_modules/pycellga/mutation/float_uniform_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/insertion_mutation.html b/_modules/pycellga/mutation/insertion_mutation.html
index 21bf1c5..05c69ad 100644
--- a/_modules/pycellga/mutation/insertion_mutation.html
+++ b/_modules/pycellga/mutation/insertion_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/shuffle_mutation.html b/_modules/pycellga/mutation/shuffle_mutation.html
index e244b60..9281a0d 100644
--- a/_modules/pycellga/mutation/shuffle_mutation.html
+++ b/_modules/pycellga/mutation/shuffle_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/swap_mutation.html b/_modules/pycellga/mutation/swap_mutation.html
index 312beb9..624ae2f 100644
--- a/_modules/pycellga/mutation/swap_mutation.html
+++ b/_modules/pycellga/mutation/swap_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/mutation/two_opt_mutation.html b/_modules/pycellga/mutation/two_opt_mutation.html
index 5e89edc..f90bf73 100644
--- a/_modules/pycellga/mutation/two_opt_mutation.html
+++ b/_modules/pycellga/mutation/two_opt_mutation.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/neighborhoods/compact_13.html b/_modules/pycellga/neighborhoods/compact_13.html
index 5d55c16..bd68ced 100644
--- a/_modules/pycellga/neighborhoods/compact_13.html
+++ b/_modules/pycellga/neighborhoods/compact_13.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/neighborhoods/compact_21.html b/_modules/pycellga/neighborhoods/compact_21.html
index 7d0133a..84a0580 100644
--- a/_modules/pycellga/neighborhoods/compact_21.html
+++ b/_modules/pycellga/neighborhoods/compact_21.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/neighborhoods/compact_25.html b/_modules/pycellga/neighborhoods/compact_25.html
index c172e75..66a8463 100644
--- a/_modules/pycellga/neighborhoods/compact_25.html
+++ b/_modules/pycellga/neighborhoods/compact_25.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/neighborhoods/compact_9.html b/_modules/pycellga/neighborhoods/compact_9.html
index 6492a87..088206b 100644
--- a/_modules/pycellga/neighborhoods/compact_9.html
+++ b/_modules/pycellga/neighborhoods/compact_9.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/neighborhoods/linear_5.html b/_modules/pycellga/neighborhoods/linear_5.html
index 7795dad..599676a 100644
--- a/_modules/pycellga/neighborhoods/linear_5.html
+++ b/_modules/pycellga/neighborhoods/linear_5.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/optimizer.html b/_modules/pycellga/optimizer.html
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-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
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 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#usage">Usage</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html b/_modules/pycellga/problems/single_objective/continuous/bohachevsky.html
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#problem-selection">Problem Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/problems/single_objective/continuous/chichinadze.html b/_modules/pycellga/problems/single_objective/continuous/chichinadze.html
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../../../usage_examples.html#example-problem">Example Problem</a></li>
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--- a/_modules/pycellga/problems/single_objective/discrete/binary/maxcut100.html
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../../contributing.html">Contributing</a><ul>
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--- a/_modules/pycellga/problems/single_objective/discrete/binary/maxcut20_01.html
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../../contributing.html">Contributing</a><ul>
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--- a/_modules/pycellga/problems/single_objective/discrete/binary/one_max.html
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#usage">Usage</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#usage">Usage</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="../../../../../../contributing.html">Contributing</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#problem-selection">Problem Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
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diff --git a/_modules/pycellga/recombination/arithmetic_crossover.html b/_modules/pycellga/recombination/arithmetic_crossover.html
index f020fa8..e1c0c51 100644
--- a/_modules/pycellga/recombination/arithmetic_crossover.html
+++ b/_modules/pycellga/recombination/arithmetic_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
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-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/blxalpha_crossover.html b/_modules/pycellga/recombination/blxalpha_crossover.html
index 7522a3d..888510d 100644
--- a/_modules/pycellga/recombination/blxalpha_crossover.html
+++ b/_modules/pycellga/recombination/blxalpha_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/byte_one_point_crossover.html b/_modules/pycellga/recombination/byte_one_point_crossover.html
index 65d6f30..5a36f40 100644
--- a/_modules/pycellga/recombination/byte_one_point_crossover.html
+++ b/_modules/pycellga/recombination/byte_one_point_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/byte_uniform_crossover.html b/_modules/pycellga/recombination/byte_uniform_crossover.html
index 166253a..7f33681 100644
--- a/_modules/pycellga/recombination/byte_uniform_crossover.html
+++ b/_modules/pycellga/recombination/byte_uniform_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/flat_crossover.html b/_modules/pycellga/recombination/flat_crossover.html
index 6c151c7..47806c1 100644
--- a/_modules/pycellga/recombination/flat_crossover.html
+++ b/_modules/pycellga/recombination/flat_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/linear_crossover.html b/_modules/pycellga/recombination/linear_crossover.html
index 3957f63..495cc89 100644
--- a/_modules/pycellga/recombination/linear_crossover.html
+++ b/_modules/pycellga/recombination/linear_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/one_point_crossover.html b/_modules/pycellga/recombination/one_point_crossover.html
index af64b3f..2674655 100644
--- a/_modules/pycellga/recombination/one_point_crossover.html
+++ b/_modules/pycellga/recombination/one_point_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/pmx_crossover.html b/_modules/pycellga/recombination/pmx_crossover.html
index 22d0c2b..a415938 100644
--- a/_modules/pycellga/recombination/pmx_crossover.html
+++ b/_modules/pycellga/recombination/pmx_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/two_point_crossover.html b/_modules/pycellga/recombination/two_point_crossover.html
index 23a2351..d2ae661 100644
--- a/_modules/pycellga/recombination/two_point_crossover.html
+++ b/_modules/pycellga/recombination/two_point_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/unfair_avarage_crossover.html b/_modules/pycellga/recombination/unfair_avarage_crossover.html
index 14cbe52..d686b81 100644
--- a/_modules/pycellga/recombination/unfair_avarage_crossover.html
+++ b/_modules/pycellga/recombination/unfair_avarage_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/recombination/uniform_crossover.html b/_modules/pycellga/recombination/uniform_crossover.html
index 63cc14f..b02c6f7 100644
--- a/_modules/pycellga/recombination/uniform_crossover.html
+++ b/_modules/pycellga/recombination/uniform_crossover.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/selection/roulette_wheel_selection.html b/_modules/pycellga/selection/roulette_wheel_selection.html
index 229d601..290c2a1 100644
--- a/_modules/pycellga/selection/roulette_wheel_selection.html
+++ b/_modules/pycellga/selection/roulette_wheel_selection.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_modules/pycellga/selection/tournament_selection.html b/_modules/pycellga/selection/tournament_selection.html
index aa42cfd..fe93789 100644
--- a/_modules/pycellga/selection/tournament_selection.html
+++ b/_modules/pycellga/selection/tournament_selection.html
@@ -291,23 +291,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="../../../usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="../../../contributing.html">Contributing</a><ul>
diff --git a/_sources/pycellga.mutation.rst.txt b/_sources/pycellga.mutation.rst.txt
index 29e367d..85ad41f 100644
--- a/_sources/pycellga.mutation.rst.txt
+++ b/_sources/pycellga.mutation.rst.txt
@@ -6,7 +6,7 @@ The `pycellga.mutation` module provides a comprehensive set of mutation operator
 This module includes the following mutation operators:
 
 **Bit Flip Mutation**
---------------
+------------------------------
 
 Applies a bitwise flip to binary-encoded chromosomes. This operator is a classic choice for binary genetic algorithms, offering a simple yet effective mutation approach.
 
@@ -16,7 +16,7 @@ Applies a bitwise flip to binary-encoded chromosomes. This operator is a classic
    :show-inheritance:
 
 **Byte-Level Mutation**
---------------
+------------------------------
 
 Performs mutations at the byte level for real-valued chromosomes. This operator leverages byte manipulation to create small, precise adjustments in the solution space, optimizing the algorithm's performance for continuous functions.
 
@@ -26,7 +26,7 @@ Performs mutations at the byte level for real-valued chromosomes. This operator
    :show-inheritance:
 
 **Randomized Byte Mutation**
---------------
+------------------------------
 
 Introduces randomness at the byte level, enabling broader exploration in real-valued optimization tasks. This operator is particularly effective when a high degree of variation is desirable.
 
@@ -36,7 +36,7 @@ Introduces randomness at the byte level, enabling broader exploration in real-va
    :show-inheritance:
 
 **Uniform Float Mutation**
---------------
+------------------------------
 
 Applies uniform random mutations across real-valued chromosomes. This operator is suitable for continuous optimization, where each gene is adjusted within a defined range to enhance solution diversity.
 
@@ -46,7 +46,7 @@ Applies uniform random mutations across real-valued chromosomes. This operator i
    :show-inheritance:
 
 **Insertion-Based Mutation**
---------------
+------------------------------
 
 A mutation strategy tailored for permutation-based representations, such as in sequencing and scheduling problems. This operator repositions a randomly selected gene within the chromosome, altering the order while preserving elements.
 
@@ -57,7 +57,7 @@ A mutation strategy tailored for permutation-based representations, such as in s
 
 
 **Shuffle Mutation**
---------------
+------------------------------
 
 Randomly rearranges a subset of genes in the chromosome. This operator is effective in permutation-based problems, promoting diversity by shuffling segments without altering individual gene values.
 
@@ -67,7 +67,7 @@ Randomly rearranges a subset of genes in the chromosome. This operator is effect
    :show-inheritance:
 
 **Swap Mutation**
---------------
+-------------------
 
 Swaps the positions of two genes, introducing subtle changes ideal for permutation-based optimizations. This operator is commonly applied in combinatorial problems where order is significant.
 
@@ -77,7 +77,7 @@ Swaps the positions of two genes, introducing subtle changes ideal for permutati
    :show-inheritance:
 
 **Two-Opt Mutation**
---------------
+----------------------
 
 A mutation operator frequently used in path optimization problems, such as the Traveling Salesman Problem. It reverses a segment of the chromosome, allowing for new path configurations without altering the gene order.
 
diff --git a/_sources/pycellga.problems.rst.txt b/_sources/pycellga.problems.rst.txt
index cb939a6..ef5b79c 100644
--- a/_sources/pycellga.problems.rst.txt
+++ b/_sources/pycellga.problems.rst.txt
@@ -5,7 +5,7 @@ The `pycellga.problems` package provides a collection of problem definitions for
 
 
 **Abstract Problem Base**
---------------
+------------------------------
 
 This module defines an abstract base class, `AbstractProblem`, which provides a standard interface for defining optimization problems. By inheriting from this base class, users can create custom problem definitions that are compatible with the rest of the `pycellga` framework. Key components of the base class include methods for evaluating objective values, setting constraints, and managing design variables.
 
@@ -15,7 +15,7 @@ This module defines an abstract base class, `AbstractProblem`, which provides a
    :show-inheritance:
 
 **Single-Objective Problems**
---------------
+------------------------------
 The `single_objective` subpackage includes a set of benchmark functions commonly used to evaluate optimization algorithms in terms of convergence speed, accuracy, and robustness. These problems are designed for scenarios where only one objective needs to be optimized.
 
 .. toctree::
diff --git a/_sources/pycellga.problems.single_objective.continuous.rst.txt b/_sources/pycellga.problems.single_objective.continuous.rst.txt
index 5ca4ffe..9b7889f 100644
--- a/_sources/pycellga.problems.single_objective.continuous.rst.txt
+++ b/_sources/pycellga.problems.single_objective.continuous.rst.txt
@@ -1,11 +1,11 @@
 Continuous Optimization Problems
-============================================
+==================================
 
 The `pycellga.problems.single_objective.continuous` package offers a range of continuous, single-objective benchmark functions. These functions are commonly used to evaluate the performance of optimization algorithms in terms of convergence accuracy, robustness, and computation speed. Below is a list of available benchmark functions in this package, each addressing unique aspects of optimization.
 
 
 **Ackley Function**
---------------
+----------------------
 A multimodal function known for its large number of local minima. Used to evaluate an algorithm's ability to escape local optima.
 
 .. automodule:: pycellga.problems.single_objective.continuous.ackley
@@ -14,7 +14,7 @@ A multimodal function known for its large number of local minima. Used to evalua
    :show-inheritance:
 
 **Bent Cigar Function**
---------------
+------------------------
 A unimodal function that is rotationally invariant, used to test convergence speed and robustness.
 
 .. automodule:: pycellga.problems.single_objective.continuous.bentcigar
@@ -23,7 +23,7 @@ A unimodal function that is rotationally invariant, used to test convergence spe
    :show-inheritance:
 
 **Bohachevsky Function**
---------------
+-------------------------
 Characterized by its simple structure with some local minima, making it ideal for testing fine-tuning capabilities.
 
 .. automodule:: pycellga.problems.single_objective.continuous.bohachevsky
@@ -32,7 +32,7 @@ Characterized by its simple structure with some local minima, making it ideal fo
    :show-inheritance:
 
 **Chichinadze Function**
---------------
+------------------------------
 A complex landscape with both smooth and steep regions, suitable for testing algorithms on challenging landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.chichinadze
@@ -41,7 +41,7 @@ A complex landscape with both smooth and steep regions, suitable for testing alg
    :show-inheritance:
 
 **Drop Wave Function**
---------------
+------------------------------
 A multimodal function often used to evaluate the balance between exploration and exploitation.
 
 .. automodule:: pycellga.problems.single_objective.continuous.dropwave
@@ -50,7 +50,7 @@ A multimodal function often used to evaluate the balance between exploration and
    :show-inheritance:
 
 **Frequency Modulation Sound Function (FMS)**
---------------
+------------------------------------------------
 A complex, multimodal function commonly used to test the robustness of optimization algorithms.
 
 .. automodule:: pycellga.problems.single_objective.continuous.fms
@@ -59,7 +59,7 @@ A complex, multimodal function commonly used to test the robustness of optimizat
    :show-inheritance:
 
 **Griewank Function**
---------------
+----------------------
 A continuous, nonlinear function with numerous local minima, commonly used to test an algorithm's global search capability.
 
 .. automodule:: pycellga.problems.single_objective.continuous.griewank
@@ -68,7 +68,7 @@ A continuous, nonlinear function with numerous local minima, commonly used to te
    :show-inheritance:
 
 **Holzman Function**
---------------
+---------------------
 An experimental function that provides various levels of difficulty for different optimization approaches.
 
 .. automodule:: pycellga.problems.single_objective.continuous.holzman
@@ -77,7 +77,7 @@ An experimental function that provides various levels of difficulty for differen
    :show-inheritance:
 
 **Levy Function**
---------------
+-------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.levy
@@ -86,7 +86,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Matyas Function**
---------------
+--------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.matyas
@@ -95,7 +95,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Pow Function**
---------------
+------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.pow
@@ -105,7 +105,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
 
 
 **Powell Function**
---------------
+---------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.powell
@@ -114,7 +114,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Rastrigin Function**
---------------
+-----------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.rastrigin
@@ -124,7 +124,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
 
 
 **Rosenbrock Function**
---------------
+------------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.rosenbrock
@@ -133,7 +133,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Rothellipsoid Function**
---------------
+----------------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.rothellipsoid
@@ -143,7 +143,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
 
 
 **Schaffer Function**
---------------
+----------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.schaffer
@@ -152,7 +152,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Schaffer2 Function**
---------------
+-----------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.schaffer2
@@ -161,7 +161,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Schwefel Function**
---------------
+----------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.schwefel
@@ -170,7 +170,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Sphere Function**
---------------
+--------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.sphere
@@ -179,7 +179,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Styblinskitang Function**
---------------
+------------------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.styblinskitang
@@ -188,7 +188,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Sumofdifferentpowers Function**
---------------
+----------------------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.sumofdifferentpowers
@@ -197,7 +197,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Threehumps Function**
---------------
+-------------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.threehumps
@@ -206,7 +206,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Zakharov Function**
---------------
+----------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.zakharov
@@ -215,7 +215,7 @@ This function is used to test the efficiency of algorithms on smooth, differenti
    :show-inheritance:
 
 **Zettle Function**
---------------
+---------------------
 This function is used to test the efficiency of algorithms on smooth, differentiable landscapes.
 
 .. automodule:: pycellga.problems.single_objective.continuous.zettle
diff --git a/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt b/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt
index 9e71cb8..b95a553 100644
--- a/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt
+++ b/_sources/pycellga.problems.single_objective.discrete.binary.rst.txt
@@ -15,7 +15,7 @@ A binary satisfaction problem, often used to evaluate an algorithm’s ability t
    :show-inheritance:
 
 **ECC Problem**
---------------
+-----------------
 
 The ECC problem tests the efficiency of algorithms in solving problems related to error-correcting codes, which have discrete solution spaces and are commonly encountered in communication systems.
 
@@ -25,7 +25,7 @@ The ECC problem tests the efficiency of algorithms in solving problems related t
    :show-inheritance:
 
 **Fletcher-Powell (FMS) Binary Problem**
---------------
+----------------------------------------------
 
 A binary version of the Fletcher-Powell function, used to evaluate robustness and efficiency in finding optimal solutions within a binary space.
 
@@ -35,7 +35,7 @@ A binary version of the Fletcher-Powell function, used to evaluate robustness an
    :show-inheritance:
 
 **Max-Cut (100 nodes)**
---------------
+------------------------------
 
 A max-cut problem involving 100 nodes, often used in graph partitioning. This problem challenges algorithms in finding optimal binary partitions.
 
@@ -45,7 +45,7 @@ A max-cut problem involving 100 nodes, often used in graph partitioning. This pr
    :show-inheritance:
 
 **Max-Cut (20 nodes, Density 0.1)**
---------------
+----------------------------------------------
 
 A max-cut problem with 20 nodes and a sparsity factor of 0.1. Suitable for testing performance on sparse graphs with limited connections.
 
@@ -55,7 +55,7 @@ A max-cut problem with 20 nodes and a sparsity factor of 0.1. Suitable for testi
    :show-inheritance:
 
 **Max-Cut (20 nodes, Density 0.9)**
---------------
+----------------------------------------------
 
 A denser version of the max-cut problem with a density of 0.9, requiring algorithms to manage numerous connections and find optimal partitions.
 
@@ -65,7 +65,7 @@ A denser version of the max-cut problem with a density of 0.9, requiring algorit
    :show-inheritance:
 
 **Multi-modal Deceptive Problem (MMDP)**
---------------
+----------------------------------------------
 
 A challenging binary problem with deceptive local optima, commonly used to assess an algorithm's ability to escape local traps in a binary landscape.
 
@@ -75,7 +75,7 @@ A challenging binary problem with deceptive local optima, commonly used to asses
    :show-inheritance:
 
 **One-Max Problem**
---------------
+------------------------------
 
 A classic benchmark in binary optimization, where the objective is to maximize the number of ones in a binary string. This problem tests the algorithm's ability to drive binary values towards an optimum.
 
@@ -85,7 +85,7 @@ A classic benchmark in binary optimization, where the objective is to maximize t
    :show-inheritance:
 
 **Peak Problem**
---------------
+------------------------------
 
 A binary optimization problem featuring multiple peaks. This problem is suitable for evaluating an algorithm’s performance in a rugged binary landscape with multiple local optima.
 
diff --git a/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt b/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt
index 97d3d7a..022f9ab 100644
--- a/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt
+++ b/_sources/pycellga.problems.single_objective.discrete.permutation.rst.txt
@@ -5,7 +5,7 @@ The `pycellga.problems.single_objective.discrete.permutation` package provides a
 
 
 **Traveling Salesman Problem (TSP)**
---------------
+----------------------------------------------
 
 The Traveling Salesman Problem (TSP) is a classic optimization problem where the goal is to find the shortest possible route that visits each city exactly once and returns to the origin city. TSP is widely used to evaluate the efficiency of algorithms that handle combinatorial and sequencing problems. This module provides a standard implementation of TSP for testing purposes.
 
diff --git a/_sources/pycellga.problems.single_objective.discrete.rst.txt b/_sources/pycellga.problems.single_objective.discrete.rst.txt
index 6f199e2..3552ef0 100644
--- a/_sources/pycellga.problems.single_objective.discrete.rst.txt
+++ b/_sources/pycellga.problems.single_objective.discrete.rst.txt
@@ -5,7 +5,7 @@ The `pycellga.problems.single_objective.discrete` package contains discrete, sin
 
 
 **Binary-Based Problems**
---------------
+------------------------------
 The `binary` subpackage contains a set of benchmark problems where solutions are represented as binary strings. These problems are widely used to test algorithms’ performance on discrete, combinatorial spaces with binary encoding. 
 
 .. toctree::
@@ -14,7 +14,7 @@ The `binary` subpackage contains a set of benchmark problems where solutions are
    pycellga.problems.single_objective.discrete.binary
 
 **Permutation-Based Problems**
---------------
+--------------------------------
 The `permutation` subpackage focuses on problems where the solution is a permutation of elements, such as sequencing or ordering tasks. This subpackage is particularly useful for evaluating algorithms that work on combinatorial optimization problems, such as the Traveling Salesman Problem (TSP).
 
 .. toctree::
diff --git a/_sources/pycellga.problems.single_objective.rst.txt b/_sources/pycellga.problems.single_objective.rst.txt
index d42898a..7ba61cb 100644
--- a/_sources/pycellga.problems.single_objective.rst.txt
+++ b/_sources/pycellga.problems.single_objective.rst.txt
@@ -4,7 +4,7 @@ Single-Objective Optimization Problems
 The `pycellga.problems.single_objective` package includes a diverse collection of single-objective optimization problems. These problems are commonly used to benchmark and evaluate the performance of optimization algorithms. The package is organized into two main subpackages, each addressing different types of problem structures: continuous and discrete.
 
 **Continuous Optimization Problems**
---------------
+---------------------------------------
 The `continuous` subpackage provides a set of continuous benchmark functions designed to evaluate algorithms on continuous search spaces. These functions are ideal for testing an algorithm’s performance in terms of accuracy, convergence speed, and robustness on smooth, differentiable landscapes.
 
 .. toctree::
@@ -13,7 +13,7 @@ The `continuous` subpackage provides a set of continuous benchmark functions des
    pycellga.problems.single_objective.continuous
 
 **Discrete Optimization Problems**
---------------
+-----------------------------------
 The `discrete` subpackage includes binary and permutation-based problems where solutions are represented as discrete values or permutations. This subpackage is particularly useful for assessing algorithms that handle combinatorial and discrete optimization challenges.
 
 .. toctree::
diff --git a/_sources/pycellga.recombination.rst.txt b/_sources/pycellga.recombination.rst.txt
index 478e05a..4f5de68 100644
--- a/_sources/pycellga.recombination.rst.txt
+++ b/_sources/pycellga.recombination.rst.txt
@@ -6,7 +6,7 @@ The `pycellga.recombination` package includes various recombination (crossover)
 
 
 **Arithmetic Crossover**
---------------
+-------------------------
 
 Performs arithmetic operations on parent genes to produce offspring. Often used in real-valued genetic algorithms to create intermediate values between parent genes.
 
@@ -16,7 +16,7 @@ Performs arithmetic operations on parent genes to produce offspring. Often used
    :show-inheritance:
 
 **BLX-Alpha Crossover**
---------------
+------------------------
 
 Generates offspring by creating genes within a specified range around parent genes, controlled by the `alpha` parameter. Effective in real-valued optimization for maintaining diversity.
 
@@ -26,7 +26,7 @@ Generates offspring by creating genes within a specified range around parent gen
    :show-inheritance:
 
 **Byte One-Point Crossover**
---------------
+------------------------------
 
 A one-point crossover operator specifically designed for byte-based chromosomes, suitable for machine-coded genetic algorithms.
 
@@ -36,7 +36,7 @@ A one-point crossover operator specifically designed for byte-based chromosomes,
    :show-inheritance:
 
 **Byte Uniform Crossover**
---------------
+---------------------------
 
 Applies uniform crossover at the byte level, allowing fine control over gene mixing in machine-coded chromosomes.
 
@@ -46,7 +46,7 @@ Applies uniform crossover at the byte level, allowing fine control over gene mix
    :show-inheritance:
 
 **Flat Crossover**
---------------
+-------------------
 
 Creates offspring by generating random values within a range defined by the parent genes. Suitable for real-valued chromosomes.
 
@@ -56,7 +56,7 @@ Creates offspring by generating random values within a range defined by the pare
    :show-inheritance:
 
 **Linear Crossover**
---------------
+---------------------
 
 Generates offspring by linearly combining genes of the parents. This operator is useful in real-valued optimization for exploring intermediate values.
 
@@ -66,7 +66,7 @@ Generates offspring by linearly combining genes of the parents. This operator is
    :show-inheritance:
 
 **One-Point Crossover**
---------------
+------------------------
 
 A classic crossover technique where a single crossover point is chosen to swap segments between parents. Commonly used in binary-encoded genetic algorithms.
 
@@ -76,7 +76,7 @@ A classic crossover technique where a single crossover point is chosen to swap s
    :show-inheritance:
 
 **Partially Matched Crossover (PMX)**
---------------
+---------------------------------------
 
 A crossover method designed for permutation-based chromosomes, such as sequencing problems. Maintains gene order by partially matching segments between parents.
 
@@ -86,7 +86,7 @@ A crossover method designed for permutation-based chromosomes, such as sequencin
    :show-inheritance:
 
 **Two-Point Crossover**
---------------
+------------------------
 
 A crossover method with two crossover points, allowing for a higher level of gene exchange between parents. Widely used in binary-encoded algorithms.
 
@@ -96,7 +96,7 @@ A crossover method with two crossover points, allowing for a higher level of gen
    :show-inheritance:
 
 **Unfair Average Crossover**
---------------
+------------------------------
 
 Calculates the average of parent genes with a slight bias, leading to offspring that retain more traits of one parent. Used in real-valued genetic algorithms.
 
@@ -106,7 +106,7 @@ Calculates the average of parent genes with a slight bias, leading to offspring
    :show-inheritance:
 
 **Uniform Crossover**
---------------
+----------------------
 
 Swaps genes randomly between parents, creating diverse offspring. Suitable for both binary and real-valued genetic representations.
 
diff --git a/_sources/pycellga.selection.rst.txt b/_sources/pycellga.selection.rst.txt
index 48fce2a..808c877 100644
--- a/_sources/pycellga.selection.rst.txt
+++ b/_sources/pycellga.selection.rst.txt
@@ -1,11 +1,11 @@
 Selection Operators
-===============================
+=====================
 
 The `pycellga.selection` package provides various selection operators that determine how individuals are chosen from the population to act as parents in genetic algorithms. Selection operators play a key role in balancing exploration and exploitation during the optimization process, directly impacting convergence and diversity.
 
 
 **Tournament Selection**
---------------
+--------------------------
 
 Implements a tournament selection method, where a subset of individuals competes, and the best-performing individual is selected as a parent. This method is useful in maintaining diversity and is effective in multimodal optimization landscapes.
 
@@ -15,7 +15,7 @@ Implements a tournament selection method, where a subset of individuals competes
    :show-inheritance:
 
 **Roulette Wheel Selection**
---------------
+-----------------------------
 
 Implements a roulette wheel selection mechanism where each individual’s chance of being selected is proportional to its fitness. This method is widely used for its simplicity and is effective in problems where fitness proportionate selection is beneficial.
 
diff --git a/_sources/usage_examples.rst.txt b/_sources/usage_examples.rst.txt
index dd9261b..6149d26 100644
--- a/_sources/usage_examples.rst.txt
+++ b/_sources/usage_examples.rst.txt
@@ -1,22 +1,16 @@
 Usage Examples
 ==============
 
-In this section, we'll explain the `cga` method in the optimizer and provide an example of how to use it. The package includes various ready-to-use crossover and mutation operators, along with real-valued, binary, and permutation functions that you can run directly. Examples for other methods are available in the `example` folder, while an example for `cga` is provided below.
+In this section, we'll explain the `cga` method in the optimizer and provide an example of how to use it.
 
-cga (Cellular Genetic Algorithm)
---------------------------------
-
-**cga** is a type of genetic algorithm where the population is structured as a grid (or other topologies), and each individual interacts only with its neighbors. This structure helps maintain diversity in the population and can prevent premature convergence. To specialize the CGA for real-valued optimization problems, ICGA (Improved CGA) with machine-coded representation can be used, applying byte operators. The encoding and decoding of numbers follow the IEEE 754 standard for floating-point arithmetic, yielding better results for continuous functions.
-
-Example Problem
----------------
+**Example Problem**
 
 Suppose we have a problem that we want to minimize using a Cellular Genetic Algorithm (CGA). The problem is defined as a simple sum of squares function, where the goal is to find a chromosome (vector) that minimizes the function.
 
 The sum of squares function computes the sum of the squares of each element in the chromosome. This function reaches its global minimum when all elements of the chromosome are equal to 0. The corresponding function value at this point is 0.
 
-ExampleProblem Class
---------------------
+**ExampleProblem Class**
+
 
 Here’s how we can define this problem in Python using the `ExampleProblem` class:
 
@@ -33,8 +27,7 @@ Here’s how we can define this problem in Python using the `ExampleProblem` cla
         def f(self, x):
             return sum(pw(xi, 2) for xi in x)
 
-Usage
------
+**Usage**
 
 .. code-block:: python
 
@@ -54,15 +47,151 @@ Usage
         maxs = [32.768] * 5,    # Maximum values for each gene
         seed_par = 100 # Ensures the random number generation is repeatable
     )
-    print("Best solution:", result[1], "\nBest solution chromosome:", result[0])
+    print("Best solution:", result.chromosome)
+    print("Best fitness value:", result.fitness_value)
 
     # The result is 
     # Best solution: 0.0 
     # Best solution chromosome: [0.0, 0.0, 0.0, 0.0, 0.0]
 
-We have provided a basic example above. If you're interested in exploring more examples:
 
-- `Click here to see the other examples <https://github.com/SevgiAkten/pycellga/tree/main/pycellga/example>`_ available directly in the repository.
+Advanced Usage Examples
+------------------------
+
+The following examples demonstrate the usage of other methods provided by `pycellga`, including `alpha_cga`, `ccga`, `mcccga`, and `sync_cga`. Each example highlights a unique algorithm solving real-world optimization problems.
+
+**Example 1: Alpha Cellular Genetic Algorithm (alpha_cga)**
+
+The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxalpha Crossover  and Float Uniform Mutation operators.
+
+.. code-block:: python
+
+    from mpmath import power as pw
+    import pycellga 
+
+    class ExampleProblem:
+        
+        def __init__(self):
+            pass
+        
+        def f(self, x):
+            return sum(pw(xi, 2) for xi in x)
+
+
+    result = pycellga.optimizer.alpha_cga(
+        n_cols=5,
+        n_rows=5,
+        n_gen=100,
+        ch_size=10,
+        gen_type=pycellga.optimizer.GeneType.REAL,
+        p_crossover=0.9,
+        p_mutation=0.2,
+        problem=ExampleProblem(),
+        selection=pycellga.optimizer.TournamentSelection,
+        recombination=pycellga.optimizer.BlxalphaCrossover,
+        mutation=pycellga.optimizer.FloatUniformMutation,
+        mins=[-15] * 10,
+        maxs=[15] * 10,
+        seed_par=100
+    )
+    print("Best solution chromosome:", result.chromosome)
+    print("Best fitness value:", result.fitness_value)
+
+---
+
+**Example 2: Compact Cellular Genetic Algorithm (ccga)**
+
+The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal is to maximize the number of `1`s.
+
+.. code-block:: python
+
+    import pycellga 
+
+    class ExampleProblem:
+        def __init__(self):
+            pass
+
+        def f(self, x):
+            return sum(x)
+
+    result = pycellga.optimizer.ccga(
+        n_cols=5,
+        n_rows=5,
+        n_gen=100,
+        ch_size=10,
+        gen_type=pycellga.optimizer.GeneType.BINARY,
+        problem=ExampleProblem(),
+        selection=pycellga.optimizer.TournamentSelection,
+        mins=[0] * 10,
+        maxs=[1] * 10
+    )
+    print("Best solution chromosome:", result.chromosome)
+    print("Best fitness value:", result.fitness_value)
+
+
+**Example 3: Machine-Coded Compact Cellular Genetic Algorithm (mcccga)**
+
+The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimization problems with machine-coded representation.
+
+.. code-block:: python
+
+    import pycellga
+
+    class RealProblem:
+        def __init__(self):
+            pass
+
+        def f(self, x):
+            return sum(np.power(xi, 2) for xi in x)
+
+    result = pycellga.optimizer.mcccga(
+        n_cols=5,
+        n_rows=5,
+        n_gen=500,
+        ch_size=5,
+        gen_type=pycellga.optimizer.GeneType.REAL,
+        problem=RealProblem(),
+        selection=pycellga.optimizer.TournamentSelection,
+        mins=[-3.55] * 5,
+        maxs=[3.55] * 5
+    )
+    print("Best solution chromosome:", result.chromosome)
+    print("Best fitness value:", result.fitness_value)
+
+
+**Example 4: Synchronous Cellular Genetic Algorithm (sync_cga)**
+
+The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a 5x5 grid using the crossover and mutation operators.
+
+.. code-block:: python
+
+    import pycellga
+
+    class ExampleProblem:
+        def __init__(self):
+            pass
+
+        def f(self, x):
+            return sum(pw(xi, 2) for xi in x)
+
+    result = pycellga.optimizer.sync_cga(
+        n_cols=5,
+        n_rows=5,
+        n_gen=100,
+        ch_size=5,
+        gen_type=pycellga.optimizer.GeneType.REAL,
+        p_crossover=0.9,
+        p_mutation=0.2,
+        problem=ExampleProblem(),
+        selection=pycellga.optimizer.TournamentSelection,
+        recombination=pycellga.optimizer.BlxalphaCrossover,
+        mutation=pycellga.optimizer.FloatUniformMutation,
+        mins=[-32.768] * 5,
+        maxs=[32.768] * 5,
+        seed_par=100
+    )
+    print("Best solution chromosome:", result.chromosome)
+    print("Best fitness value:", result.fitness_value)
 
 
 Customization Scenarios
@@ -70,8 +199,7 @@ Customization Scenarios
 
 `pycellga` allows users to customize the methods and operators used in their optimization problems. Below are some examples to help you design your own scenarios.
 
-Selecting Gene Types
----------------------
+**Selecting Gene Types**
 
 You can change the `gen_type` parameter based on the type of problem:
 
@@ -80,13 +208,11 @@ You can change the `gen_type` parameter based on the type of problem:
 - `pycellga.optimizer.GeneType.PERMUTATION` for permutation-based problems.
 
 
-Problem Selection
-------------------
+**Problem Selection**
 
 `pycellga` includes several built-in problems for quick usage, categorized into continuous (real-valued), binary, and permutation problems. These problems allow users to directly specify the `problem` parameter for quick implementation.
 
-Continuous Problems
---------------------
+**Continuous Problems**
 
 For continuous optimization problems, the following functions are available:
 
@@ -115,8 +241,7 @@ For continuous optimization problems, the following functions are available:
 - `pycellga.optimizer.Zakharov()`
 - `pycellga.optimizer.Zettle()`
 
-Binary Problems
-----------------
+**Binary Problems**
 
 For binary optimization problems, use the following built-in options:
 
@@ -129,8 +254,7 @@ For binary optimization problems, use the following built-in options:
 - `pycellga.optimizer.OneMax()`
 - `pycellga.optimizer.Peak()`
 
-Permutation Problems
---------------------
+**Permutation Problems**
 
 For permutation-based optimization problems, the following option is available:
 
@@ -139,15 +263,14 @@ For permutation-based optimization problems, the following option is available:
 These built-in problems provide a diverse set of test cases, allowing users to explore `pycellga`'s capabilities across a wide range of optimization challenges. Users can also define custom problems to suit their specific needs.
 
 
-Selection Operators
---------------------
+**Selection Operators**
+
 Choose from a variety of selection methods:
 
 - `pycellga.optimizer.TournamentSelection`
 - `pycellga.optimizer.RouletteWheelSelection`
 
-Recombination Operators
-------------------------
+**Recombination Operators**
 
 The package provides multiple crossover operators:
 
@@ -163,8 +286,7 @@ The package provides multiple crossover operators:
 - `pycellga.optimizer.LinearCrossover`
 - `pycellga.optimizer.UnfairAvarageCrossover`
 
-Mutation Operators
--------------------
+**Mutation Operators**
 
 You can customize mutation with these options:
 
@@ -179,11 +301,9 @@ You can customize mutation with these options:
 - `pycellga.optimizer.MutationOperator`
 
 
-Example Scenarios
-------------------
+**Example Scenarios**
 
-Scenario 1: Binary Optimization with Tournament Selection
-------------------------------------------------------------
+**Scenario 1: Binary Optimization with Tournament Selection**
 
 Optimize a binary problem using tournament selection, one-point crossover, and bit-flip mutation.
 
@@ -209,8 +329,7 @@ Optimize a binary problem using tournament selection, one-point crossover, and b
     print("Best fitness value:", result.fitness_value)
 
 
-Scenario 2: Real-Valued Optimization with Byte One Point Crossover
---------------------------------------------------------------------
+**Scenario 2: Real-Valued Optimization with Byte One Point Crossover**
 
 Solve a real-valued optimization problem using Byte One Point Crossover and Byte Mutation.
 
@@ -235,8 +354,7 @@ Solve a real-valued optimization problem using Byte One Point Crossover and Byte
     print("Best solution:", result.chromosome)
     print("Best fitness value:", result.fitness_value)
 
-Scenario 3: Permutation Optimization for Traveling Salesman Problem
-----------------------------------------------------------------------
+**Scenario 3: Permutation Optimization for Traveling Salesman Problem**
 
 Optimize a TSP using permutation encoding, PMX crossover, and swap mutation.
 
diff --git a/citing.html b/citing.html
index 5c4563a..446713b 100644
--- a/citing.html
+++ b/citing.html
@@ -293,23 +293,8 @@
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
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index 2eaee8e..abcad13 100644
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@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
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 <li class="toctree-l1 current"><a class="current reference internal" href="#">Contributing</a><ul>
diff --git a/genindex.html b/genindex.html
index e506f92..45b052d 100644
--- a/genindex.html
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@@ -291,23 +291,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/index.html b/index.html
index d834dea..2cdc016 100644
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@@ -293,23 +293,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/installation.html b/installation.html
index a6577f6..226de57 100644
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@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
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diff --git a/py-modindex.html b/py-modindex.html
index f053933..6d5d262 100644
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
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 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.example.html b/pycellga.example.html
index 74252ff..ae9ae3e 100644
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@@ -294,23 +294,8 @@
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 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.html b/pycellga.html
index 41ef233..a8eca4f 100644
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
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 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.mutation.html b/pycellga.mutation.html
index cb01922..ef24b6d 100644
--- a/pycellga.mutation.html
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@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.neighborhoods.html b/pycellga.neighborhoods.html
index 2b8b11c..446aed3 100644
--- a/pycellga.neighborhoods.html
+++ b/pycellga.neighborhoods.html
@@ -294,23 +294,8 @@
 </ul>
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 <li class="toctree-l1"><a class="reference internal" href="usage_examples.html">Usage Examples</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.problems.html b/pycellga.problems.html
index d7d2d7b..1c864c3 100644
--- a/pycellga.problems.html
+++ b/pycellga.problems.html
@@ -294,23 +294,8 @@
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 <li class="toctree-l1"><a class="reference internal" href="usage_examples.html">Usage Examples</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
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index c9850ee..561e7e9 100644
--- a/pycellga.problems.single_objective.continuous.html
+++ b/pycellga.problems.single_objective.continuous.html
@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
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 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
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index e8ef545..1c522ef 100644
--- a/pycellga.problems.single_objective.discrete.binary.html
+++ b/pycellga.problems.single_objective.discrete.binary.html
@@ -294,23 +294,8 @@
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 <li class="toctree-l1"><a class="reference internal" href="usage_examples.html">Usage Examples</a><ul>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
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 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.problems.single_objective.discrete.html b/pycellga.problems.single_objective.discrete.html
index 8372960..94ece60 100644
--- a/pycellga.problems.single_objective.discrete.html
+++ b/pycellga.problems.single_objective.discrete.html
@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.problems.single_objective.discrete.permutation.html b/pycellga.problems.single_objective.discrete.permutation.html
index 958a287..9f45983 100644
--- a/pycellga.problems.single_objective.discrete.permutation.html
+++ b/pycellga.problems.single_objective.discrete.permutation.html
@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
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 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/pycellga.problems.single_objective.html b/pycellga.problems.single_objective.html
index 16efb28..a9545dd 100644
--- a/pycellga.problems.single_objective.html
+++ b/pycellga.problems.single_objective.html
@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
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 </li>
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@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
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index d5ad374..b8e08d4 100644
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+++ b/pycellga.selection.html
@@ -294,23 +294,8 @@
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
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-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
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index 91ff26f..ff6be51 100644
--- a/searchindex.js
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\ No newline at end of file
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 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="usage_examples.html">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="usage_examples.html#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="usage_examples.html#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
diff --git a/usage_examples.html b/usage_examples.html
index 275d5db..5ae36dd 100644
--- a/usage_examples.html
+++ b/usage_examples.html
@@ -294,23 +294,8 @@
 </ul>
 </li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Usage Examples</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#cga-cellular-genetic-algorithm">cga (Cellular Genetic Algorithm)</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#example-problem">Example Problem</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#exampleproblem-class">ExampleProblem Class</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#usage">Usage</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#advanced-usage-examples">Advanced Usage Examples</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#customization-scenarios">Customization Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#selecting-gene-types">Selecting Gene Types</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#problem-selection">Problem Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#continuous-problems">Continuous Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#binary-problems">Binary Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#permutation-problems">Permutation Problems</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#selection-operators">Selection Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#recombination-operators">Recombination Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#mutation-operators">Mutation Operators</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#example-scenarios">Example Scenarios</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#scenario-1-binary-optimization-with-tournament-selection">Scenario 1: Binary Optimization with Tournament Selection</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#scenario-2-real-valued-optimization-with-byte-one-point-crossover">Scenario 2: Real-Valued Optimization with Byte One Point Crossover</a></li>
-<li class="toctree-l2"><a class="reference internal" href="#scenario-3-permutation-optimization-for-traveling-salesman-problem">Scenario 3: Permutation Optimization for Traveling Salesman Problem</a></li>
 </ul>
 </li>
 <li class="toctree-l1"><a class="reference internal" href="contributing.html">Contributing</a><ul>
@@ -351,18 +336,11 @@
              
   <section id="usage-examples">
 <h1>Usage Examples<a class="headerlink" href="#usage-examples" title="Link to this heading"></a></h1>
-<p>In this section, we’ll explain the <cite>cga</cite> method in the optimizer and provide an example of how to use it. The package includes various ready-to-use crossover and mutation operators, along with real-valued, binary, and permutation functions that you can run directly. Examples for other methods are available in the <cite>example</cite> folder, while an example for <cite>cga</cite> is provided below.</p>
-<section id="cga-cellular-genetic-algorithm">
-<h2>cga (Cellular Genetic Algorithm)<a class="headerlink" href="#cga-cellular-genetic-algorithm" title="Link to this heading"></a></h2>
-<p><strong>cga</strong> is a type of genetic algorithm where the population is structured as a grid (or other topologies), and each individual interacts only with its neighbors. This structure helps maintain diversity in the population and can prevent premature convergence. To specialize the CGA for real-valued optimization problems, ICGA (Improved CGA) with machine-coded representation can be used, applying byte operators. The encoding and decoding of numbers follow the IEEE 754 standard for floating-point arithmetic, yielding better results for continuous functions.</p>
-</section>
-<section id="example-problem">
-<h2>Example Problem<a class="headerlink" href="#example-problem" title="Link to this heading"></a></h2>
+<p>In this section, we’ll explain the <cite>cga</cite> method in the optimizer and provide an example of how to use it.</p>
+<p><strong>Example Problem</strong></p>
 <p>Suppose we have a problem that we want to minimize using a Cellular Genetic Algorithm (CGA). The problem is defined as a simple sum of squares function, where the goal is to find a chromosome (vector) that minimizes the function.</p>
 <p>The sum of squares function computes the sum of the squares of each element in the chromosome. This function reaches its global minimum when all elements of the chromosome are equal to 0. The corresponding function value at this point is 0.</p>
-</section>
-<section id="exampleproblem-class">
-<h2>ExampleProblem Class<a class="headerlink" href="#exampleproblem-class" title="Link to this heading"></a></h2>
+<p><strong>ExampleProblem Class</strong></p>
 <p>Here’s how we can define this problem in Python using the <cite>ExampleProblem</cite> class:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mpmath</span> <span class="kn">import</span> <span class="n">power</span> <span class="k">as</span> <span class="n">pw</span>
 <span class="kn">import</span> <span class="nn">pycellga</span>
@@ -376,9 +354,7 @@ <h2>ExampleProblem Class<a class="headerlink" href="#exampleproblem-class" title
         <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">pw</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span>
 </pre></div>
 </div>
-</section>
-<section id="usage">
-<h2>Usage<a class="headerlink" href="#usage" title="Link to this heading"></a></h2>
+<p><strong>Usage</strong></p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">cga</span><span class="p">(</span>
     <span class="n">n_cols</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
     <span class="n">n_rows</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
@@ -395,37 +371,149 @@ <h2>Usage<a class="headerlink" href="#usage" title="Link to this heading"></a
     <span class="n">maxs</span> <span class="o">=</span> <span class="p">[</span><span class="mf">32.768</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span>    <span class="c1"># Maximum values for each gene</span>
     <span class="n">seed_par</span> <span class="o">=</span> <span class="mi">100</span> <span class="c1"># Ensures the random number generation is repeatable</span>
 <span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Best solution chromosome:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">chromosome</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
 
 <span class="c1"># The result is</span>
 <span class="c1"># Best solution: 0.0</span>
 <span class="c1"># Best solution chromosome: [0.0, 0.0, 0.0, 0.0, 0.0]</span>
 </pre></div>
 </div>
-<p>We have provided a basic example above. If you’re interested in exploring more examples:</p>
-<ul class="simple">
-<li><p><a class="reference external" href="https://github.com/SevgiAkten/pycellga/tree/main/pycellga/example">Click here to see the other examples</a> available directly in the repository.</p></li>
-</ul>
+<section id="advanced-usage-examples">
+<h2>Advanced Usage Examples<a class="headerlink" href="#advanced-usage-examples" title="Link to this heading"></a></h2>
+<p>The following examples demonstrate the usage of other methods provided by <cite>pycellga</cite>, including <cite>alpha_cga</cite>, <cite>ccga</cite>, <cite>mcccga</cite>, and <cite>sync_cga</cite>. Each example highlights a unique algorithm solving real-world optimization problems.</p>
+<p><strong>Example 1: Alpha Cellular Genetic Algorithm (alpha_cga)</strong></p>
+<p>The Alpha Cellular Genetic Algorithm optimizes a real-valued problem using Blxalpha Crossover  and Float Uniform Mutation operators.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">mpmath</span> <span class="kn">import</span> <span class="n">power</span> <span class="k">as</span> <span class="n">pw</span>
+<span class="kn">import</span> <span class="nn">pycellga</span>
+
+<span class="k">class</span> <span class="nc">ExampleProblem</span><span class="p">:</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">pw</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span>
+
+
+<span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">alpha_cga</span><span class="p">(</span>
+    <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_gen</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">ch_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">gen_type</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">GeneType</span><span class="o">.</span><span class="n">REAL</span><span class="p">,</span>
+    <span class="n">p_crossover</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
+    <span class="n">p_mutation</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+    <span class="n">problem</span><span class="o">=</span><span class="n">ExampleProblem</span><span class="p">(),</span>
+    <span class="n">selection</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">TournamentSelection</span><span class="p">,</span>
+    <span class="n">recombination</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">BlxalphaCrossover</span><span class="p">,</span>
+    <span class="n">mutation</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">FloatUniformMutation</span><span class="p">,</span>
+    <span class="n">mins</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">15</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="n">maxs</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="n">seed_par</span><span class="o">=</span><span class="mi">100</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution chromosome:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">chromosome</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>—</p>
+<p><strong>Example 2: Compact Cellular Genetic Algorithm (ccga)</strong></p>
+<p>The Compact Cellular Genetic Algorithm optimizes a binary problem where the goal is to maximize the number of <a href="#id1"><span class="problematic" id="id2">`</span></a>1`s.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pycellga</span>
+
+<span class="k">class</span> <span class="nc">ExampleProblem</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+
+<span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">ccga</span><span class="p">(</span>
+    <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_gen</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">ch_size</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+    <span class="n">gen_type</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">GeneType</span><span class="o">.</span><span class="n">BINARY</span><span class="p">,</span>
+    <span class="n">problem</span><span class="o">=</span><span class="n">ExampleProblem</span><span class="p">(),</span>
+    <span class="n">selection</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">TournamentSelection</span><span class="p">,</span>
+    <span class="n">mins</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="n">maxs</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="mi">10</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution chromosome:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">chromosome</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><strong>Example 3: Machine-Coded Compact Cellular Genetic Algorithm (mcccga)</strong></p>
+<p>The Machine-Coded Compact Cellular Genetic Algorithm solves real-valued optimization problems with machine-coded representation.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pycellga</span>
+
+<span class="k">class</span> <span class="nc">RealProblem</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">mcccga</span><span class="p">(</span>
+    <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_gen</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span>
+    <span class="n">ch_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">gen_type</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">GeneType</span><span class="o">.</span><span class="n">REAL</span><span class="p">,</span>
+    <span class="n">problem</span><span class="o">=</span><span class="n">RealProblem</span><span class="p">(),</span>
+    <span class="n">selection</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">TournamentSelection</span><span class="p">,</span>
+    <span class="n">mins</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">3.55</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span>
+    <span class="n">maxs</span><span class="o">=</span><span class="p">[</span><span class="mf">3.55</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution chromosome:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">chromosome</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><strong>Example 4: Synchronous Cellular Genetic Algorithm (sync_cga)</strong></p>
+<p>The Synchronous Cellular Genetic Algorithm optimizes a real-valued problem in a 5x5 grid using the crossover and mutation operators.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pycellga</span>
+
+<span class="k">class</span> <span class="nc">ExampleProblem</span><span class="p">:</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">pass</span>
+
+    <span class="k">def</span> <span class="nf">f</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">pw</span><span class="p">(</span><span class="n">xi</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">xi</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span>
+
+<span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">sync_cga</span><span class="p">(</span>
+    <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">n_gen</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">ch_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+    <span class="n">gen_type</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">GeneType</span><span class="o">.</span><span class="n">REAL</span><span class="p">,</span>
+    <span class="n">p_crossover</span><span class="o">=</span><span class="mf">0.9</span><span class="p">,</span>
+    <span class="n">p_mutation</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span>
+    <span class="n">problem</span><span class="o">=</span><span class="n">ExampleProblem</span><span class="p">(),</span>
+    <span class="n">selection</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">TournamentSelection</span><span class="p">,</span>
+    <span class="n">recombination</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">BlxalphaCrossover</span><span class="p">,</span>
+    <span class="n">mutation</span><span class="o">=</span><span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">FloatUniformMutation</span><span class="p">,</span>
+    <span class="n">mins</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mf">32.768</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span>
+    <span class="n">maxs</span><span class="o">=</span><span class="p">[</span><span class="mf">32.768</span><span class="p">]</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span>
+    <span class="n">seed_par</span><span class="o">=</span><span class="mi">100</span>
+<span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best solution chromosome:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">chromosome</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
+</pre></div>
+</div>
 </section>
 <section id="customization-scenarios">
 <h2>Customization Scenarios<a class="headerlink" href="#customization-scenarios" title="Link to this heading"></a></h2>
 <p><cite>pycellga</cite> allows users to customize the methods and operators used in their optimization problems. Below are some examples to help you design your own scenarios.</p>
-</section>
-<section id="selecting-gene-types">
-<h2>Selecting Gene Types<a class="headerlink" href="#selecting-gene-types" title="Link to this heading"></a></h2>
+<p><strong>Selecting Gene Types</strong></p>
 <p>You can change the <cite>gen_type</cite> parameter based on the type of problem:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.GeneType.REAL</cite> for real-valued problems.</p></li>
 <li><p><cite>pycellga.optimizer.GeneType.BINARY</cite> for binary problems.</p></li>
 <li><p><cite>pycellga.optimizer.GeneType.PERMUTATION</cite> for permutation-based problems.</p></li>
 </ul>
-</section>
-<section id="problem-selection">
-<h2>Problem Selection<a class="headerlink" href="#problem-selection" title="Link to this heading"></a></h2>
+<p><strong>Problem Selection</strong></p>
 <p><cite>pycellga</cite> includes several built-in problems for quick usage, categorized into continuous (real-valued), binary, and permutation problems. These problems allow users to directly specify the <cite>problem</cite> parameter for quick implementation.</p>
-</section>
-<section id="continuous-problems">
-<h2>Continuous Problems<a class="headerlink" href="#continuous-problems" title="Link to this heading"></a></h2>
+<p><strong>Continuous Problems</strong></p>
 <p>For continuous optimization problems, the following functions are available:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.Ackley()</cite></p></li>
@@ -453,9 +541,7 @@ <h2>Continuous Problems<a class="headerlink" href="#continuous-problems" title="
 <li><p><cite>pycellga.optimizer.Zakharov()</cite></p></li>
 <li><p><cite>pycellga.optimizer.Zettle()</cite></p></li>
 </ul>
-</section>
-<section id="binary-problems">
-<h2>Binary Problems<a class="headerlink" href="#binary-problems" title="Link to this heading"></a></h2>
+<p><strong>Binary Problems</strong></p>
 <p>For binary optimization problems, use the following built-in options:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.CountSat()</cite></p></li>
@@ -467,25 +553,19 @@ <h2>Binary Problems<a class="headerlink" href="#binary-problems" title="Link to
 <li><p><cite>pycellga.optimizer.OneMax()</cite></p></li>
 <li><p><cite>pycellga.optimizer.Peak()</cite></p></li>
 </ul>
-</section>
-<section id="permutation-problems">
-<h2>Permutation Problems<a class="headerlink" href="#permutation-problems" title="Link to this heading"></a></h2>
+<p><strong>Permutation Problems</strong></p>
 <p>For permutation-based optimization problems, the following option is available:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.Tsp()</cite></p></li>
 </ul>
 <p>These built-in problems provide a diverse set of test cases, allowing users to explore <cite>pycellga</cite>’s capabilities across a wide range of optimization challenges. Users can also define custom problems to suit their specific needs.</p>
-</section>
-<section id="selection-operators">
-<h2>Selection Operators<a class="headerlink" href="#selection-operators" title="Link to this heading"></a></h2>
+<p><strong>Selection Operators</strong></p>
 <p>Choose from a variety of selection methods:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.TournamentSelection</cite></p></li>
 <li><p><cite>pycellga.optimizer.RouletteWheelSelection</cite></p></li>
 </ul>
-</section>
-<section id="recombination-operators">
-<h2>Recombination Operators<a class="headerlink" href="#recombination-operators" title="Link to this heading"></a></h2>
+<p><strong>Recombination Operators</strong></p>
 <p>The package provides multiple crossover operators:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.OnePointCrossover</cite></p></li>
@@ -500,9 +580,7 @@ <h2>Recombination Operators<a class="headerlink" href="#recombination-operators"
 <li><p><cite>pycellga.optimizer.LinearCrossover</cite></p></li>
 <li><p><cite>pycellga.optimizer.UnfairAvarageCrossover</cite></p></li>
 </ul>
-</section>
-<section id="mutation-operators">
-<h2>Mutation Operators<a class="headerlink" href="#mutation-operators" title="Link to this heading"></a></h2>
+<p><strong>Mutation Operators</strong></p>
 <p>You can customize mutation with these options:</p>
 <ul class="simple">
 <li><p><cite>pycellga.optimizer.BitFlipMutation</cite></p></li>
@@ -515,12 +593,8 @@ <h2>Mutation Operators<a class="headerlink" href="#mutation-operators" title="Li
 <li><p><cite>pycellga.optimizer.FloatUniformMutation</cite></p></li>
 <li><p><cite>pycellga.optimizer.MutationOperator</cite></p></li>
 </ul>
-</section>
-<section id="example-scenarios">
-<h2>Example Scenarios<a class="headerlink" href="#example-scenarios" title="Link to this heading"></a></h2>
-</section>
-<section id="scenario-1-binary-optimization-with-tournament-selection">
-<h2>Scenario 1: Binary Optimization with Tournament Selection<a class="headerlink" href="#scenario-1-binary-optimization-with-tournament-selection" title="Link to this heading"></a></h2>
+<p><strong>Example Scenarios</strong></p>
+<p><strong>Scenario 1: Binary Optimization with Tournament Selection</strong></p>
 <p>Optimize a binary problem using tournament selection, one-point crossover, and bit-flip mutation.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">cga</span><span class="p">(</span>
     <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
@@ -542,9 +616,7 @@ <h2>Scenario 1: Binary Optimization with Tournament Selection<a class="headerlin
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
 </pre></div>
 </div>
-</section>
-<section id="scenario-2-real-valued-optimization-with-byte-one-point-crossover">
-<h2>Scenario 2: Real-Valued Optimization with Byte One Point Crossover<a class="headerlink" href="#scenario-2-real-valued-optimization-with-byte-one-point-crossover" title="Link to this heading"></a></h2>
+<p><strong>Scenario 2: Real-Valued Optimization with Byte One Point Crossover</strong></p>
 <p>Solve a real-valued optimization problem using Byte One Point Crossover and Byte Mutation.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">cga</span><span class="p">(</span>
     <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
@@ -566,9 +638,7 @@ <h2>Scenario 2: Real-Valued Optimization with Byte One Point Crossover<a class="
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Best fitness value:&quot;</span><span class="p">,</span> <span class="n">result</span><span class="o">.</span><span class="n">fitness_value</span><span class="p">)</span>
 </pre></div>
 </div>
-</section>
-<section id="scenario-3-permutation-optimization-for-traveling-salesman-problem">
-<h2>Scenario 3: Permutation Optimization for Traveling Salesman Problem<a class="headerlink" href="#scenario-3-permutation-optimization-for-traveling-salesman-problem" title="Link to this heading"></a></h2>
+<p><strong>Scenario 3: Permutation Optimization for Traveling Salesman Problem</strong></p>
 <p>Optimize a TSP using permutation encoding, PMX crossover, and swap mutation.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">result</span> <span class="o">=</span> <span class="n">pycellga</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">cga</span><span class="p">(</span>
     <span class="n">n_cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>